Document image data resulting from scanning of a hardcopy document is often stored in the form of multiple scanlines, each scanline comprising multiple pixels. Document images generally contain multiple regions with each region exhibiting distinct properties. When processing this type of image data, it is helpful to know the type of image represented by the data. For example, the image data could represent graphics, text, a halftone, contone, or some other recognized image type. A page of image data could be all one type, or some combination of image types. To process document images containing multiple regions accurately, different algorithms should be applied to each type of region. For example, text regions need to be sharpened before being printed. However, halftone pictures need to be low-pass filtered first to avoid moiré. Therefore, a document image generally needs to be segmented into its constituent regions before image processing techniques can be applied most effectively.
It is known in the art to take a page of document image data and to separate the image data into windows of similar image types. For instance, a page of image data may include a halftoned picture with accompanying text describing the picture. In order to efficiently process the image data, it is known to separate the page of document image data into two windows, a first window representing the halftoned image, and a second window representing the text. Processing of the page of document image data can then be efficiently carried out by tailoring the processing to the type of image data being processed.
Traditional methods of document image segmentation, such as for example, U.S. Pat. No. 5,850,474 to Fan et al. for Apparatus and Method for Segmenting and Classifying Image Data, use heuristic rules to classify each pixel, then use connected component analysis to form “windows” of similar image types. D/A1159 describes an alternative approach, called the BISEG algorithm, where windows are generated by growing the “background”. This method is applicable for document images where the “windows” are separated by a uniform background. To complete image segmentation, each window must be classified into contone or halftone, and if halftone the frequency of the halftone screen must be detected. Previous algorithms tend to be complicated, and difficult to implement. What is needed is a simple method for classifying an image as contone or halftone, and if halftone, determining the halftone frequency.